SC Solutions brings space and AI technologies to the agricultural sector

The approach is based on integrating remote sensing data with advanced analyses, using machine learning systems, to promote more advanced agricultural practices and contribute to environmental conservation efforts.

According to Cliff Beek, CEO of SC Solutions, the company is committed to driving change in the agricultural sector. “Our expansion into the agricultural and environmental domains is a significant milestone. We believe that technology can play a transformative role in modernising agricultural practices, making them more sustainable and efficient. This move is in line with our mission to innovate to bring improvements to businesses and the environment.”

Central to SC Solutions’ approach is the combination of remote sensing data and advanced analyses, including machine learning, to carry out complex prediction and classification tasks, guaranteeing farmers accurate information to optimise crop management.

The company’s main areas of activity in the agricultural sector are comprehensive field monitoring and yield forecasting for maize plantations, efficient classification of sugar cane crops and monitoring the health of vineyards.

SC Solutions has also developed a proprietary algorithm capable of accurately determining carbon mass directly from satellite images. This advance allows it to help customers transform crops into renewable agricultural products. By harnessing carbon incentives, the solutions promote sustainable and environmentally friendly agricultural practices, contributing significantly to environmental conservation efforts.

“Our solutions are developed not only to raise agricultural efficiency levels, but also to generate positive impacts on the environment. We’re excited about the potential of our technology to boost agricultural sustainability and contribute to a greener, more resilient future,” adds Beek.

Unbeatable duo?

Several agricultural systems suppliers are developing innovative solutions to boost agricultural productivity. Deere, for example, plans to launch a fully autonomous system for corn and soya production by 2030, starting with an autonomous tractor.

With this and other autonomous technologies, it will be possible to reduce agricultural costs associated with seeds, fertilizers, and chemicals involved in farming by around 27%, according to the research. This reduction should mostly be driven by a 60 per cent decline in fertilizer use and a 67 per cent reduction in herbicide costs.

Furthermore, as autonomous technologies replace human labour, labour costs could fall by around 85%. The research also suggests that predictive maintenance could reduce fuel, lubricant, electricity, and repair costs by approximately 20 per cent.

The following graph takes as an example corn, soya, and wheat crops in the United States that use a combination of precision farming and AI practices and manage to reduce operating costs by 26%, 31% and 31% respectively per acre.

ARK estimates that 75 to 80 per cent of crops will adopt this class of solutions worldwide, with different adoption rates depending on factors such as crop size, cultural differences and connectivity resources. Operating at scale, precision farming and AI tools could reduce operating costs as a percentage of sales from 42 per cent to 33 per cent, potentially generating a global usable market of around $67 billion, as shown below. If the use of autonomous technologies becomes widespread, agricultural companies could generate recurring revenue streams with margins similar to those of software as a service.